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  Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics

Thierbach, K., Bazin, P.-L., De Back, W., Gavriilidis, F., Kirilina, E., Jäger, C., et al. (2018). Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics. BioRxiv. doi:10.1101/297689.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-130B-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9998-5
Genre: Paper

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 Creators:
Thierbach, Konstantin1, Author              
Bazin, Pierre-Louis2, Author              
De Back , Walter 3, Author
Gavriilidis, Filippos1, Author              
Kirilina, Evgeniya1, Author              
Jäger, Carsten1, Author              
Morawski, Markus 4, Author
Geyer, Stefan1, Author              
Weiskopf, Nikolaus1, Author              
Scherf, Nico1, Author              
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
2Netherlands Institute for Neuroscience, Amsterdam, the Netherlands, ou_persistent22              
3Institute for Medical Informatics and Biometry, TU Dresden, Germany, ou_persistent22              
4Paul Flechsig Institute of Brain Research, University of Leipzig, Germany , ou_persistent22              

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 Abstract: Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmenta- tion of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.

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Language(s): eng - English
 Dates: 2018-04-09
 Publication Status: Published online
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 Rev. Method: No review
 Identifiers: DOI: 10.1101/297689
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Title: BioRxiv
Source Genre: Journal
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